Hi everyone, my name is Troy, and I am a machine learning engineer who works pretty closely with generative AI day in and day out. I obviously use Claude Code very extensively, along with some other coding agents, and I have had this idea that has been eating away at me for some time. Right now, everybody is trying to use semantic retrieval to give agents better context and knowledge. Retrieval-augmented generation has been around for a long time, and it has been kind of the way we get agents information. That has changed a little over time with tool calling, functions, and things of that nature, but fundamentally we are still trying to get AI to adapt to a 50-year-old system of storing files that does not work well with how AI actually works. Every time I use Claude (or any other agent) for real work, I watch it burn massive amounts of tokens and time just doing recon. It greps, reads flat files, and tries to reconstruct the codebase in its head. It is like handing a master builder 10,000 sticky notes instead of a blueprint. The research is starting to back this up: A recent ICLR 2026 paper (“How Do Coding Agents Spend Your Money?”) found that input tokens completely dominate overall cost because agents keep re-reading files just to orient themselves. Cursor recently wrote about scaling long-running autonomous agents and disclosed burning trillions of tokens in the process. Foundation Capital just published a thesis calling context graphs a trillion-dollar opportunity because current systems lack the structural relationships AI actually needs. I was wrestling with keeping this proprietary, but I decided this is something the world needs. I am probably being overly ambitious, but I meticulously planned this out and built the first alpha version of what I think can fundamentally change how we interact with AI. So I built Kin to fix the substrate. It is a semantic repo layer. Instead of flat files, the semantic relationships are the actual source of truth, and your standard Git files are just backward compatible surfaces. It gives Claude (or any other agent) a native structural blueprint over MCP. Under the hood, it runs on KinDB , a Rust graph and retrieval engine I wrote. I ran a 70-task validated benchmark against raw Git exploration. Kin won 69 out of 70 times using 50.0 percent less wall clock time and 44.6 percent fewer tokens . That validated sweep was done with Codex CLI because that eval path is fully checked end to end. Claude Code validation is next, and I am not going to overclaim on performance until that harness is rock solid. It is open source under Apache 2.0. Repos: Kin KinDB If anyone thinks this is a good idea, I would love some help breaking it, testing it, and really making it a must-have tool. I truly believe this can not only improve the capabilities of AI but also drastically reduce the massive compute costs and help alleviate the hardware and chip scarcity we are all watching unfold right now. If you use Claude (or any other agent) for real work, try the MCP surface, tell me where the context still feels too wide, and tell me what a fair Claude Code benchmark should actually measure. submitted by /u/troyjr4103
Originally posted by u/troyjr4103 on r/ClaudeCode